Technical Field
The present invention generally relates to information processing and more particularly to updating an attenuation coefficient for a model corresponding to time-series input data.
Description of the Related Art
Conventionally, it has been known that neural networks, dynamic Boltzmann machines, and the like are capable of learning models corresponding to time-series input data. In particular, it is expected that a dynamic Boltzmann machine can realize a high learning ability using machine learning.
When learning such a model corresponding to time-series input data, coefficients have been used that increase or decrease in time-series based on a predetermined expression, rate of change, or the like. However, when such a coefficient is used, there are cases where the learning time fluctuates significantly according to the initial value of the coefficient, and in such cases, it has been necessary to set a suitable initial value for the coefficient.